Latent variable scores correlated with mean MetaViper scores of drugs
Sara Gosline
2019-12-17
Here, we are evaluating the ‘druggability’ of viper proteins that are well correlated with variables. The plots at the end of this are labeled by LVs, but the enrichment is actually based on the list of viper proteins that have a correlation of 0.5 or greater with LV expression across all tumors.
Import packages
First, import packages to process and plot the data.
library(dplyr)
library(tidyr)
library(readr)
library(synapser)
library(feather)
synLogin()## Welcome, Sara Gosline!## NULL#get viper scores
viper_res <- synTableQuery('SELECT * FROM syn21259610')$filepath %>%
readr::read_csv()%>%select(-c(ROW_ID,ROW_VERSION))##
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top_lvs<-synTableQuery('SELECT distinct latent_var FROM syn21416832')$asDataFrame()$latent_var##
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mp_res <-synTableQuery("SELECT * from syn21046991 where tumorType <> 'NA'")$filepath %>%
readr::read_csv()%>%
select(latent_var,value,tumorType,specimenID,studyName)%>%
subset(latent_var%in%top_lvs)##
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drug_targets <- feather::read_feather(synGet('syn20700199')$path)
drug_targets <- drug_targets %>%
filter(mean_pchembl > 7) %>%
mutate(gene= hugo_gene) %>%
select(gene, std_name) Map drugs and targets to summarize mapping
For each drug we want to compute the mean metaviper score of each of its targets in each of the samples to provide a per-sample analysis of drug efficacy.
term2viper <- drug_targets %>%
mutate(term = std_name) %>%
select(term, gene) %>%
distinct()%>%
left_join(viper_res,by='gene')
library(ggplot2)
drugTargs<-term2viper%>%
group_by(term)%>%
summarize(numTargs=n_distinct(gene))
ggplot(drugTargs)+geom_bar(aes(x=numTargs))
Most drugs have only 1 target! Let’s filter to those drugs that have at least 3 targets
Calculate mean drug metaviper values and correlation with LV
topDrugs<-subset(drugTargs,numTargs>2)%>%
select(term)
drugMeans<-term2viper%>%
subset(term%in%topDrugs$term)%>%
group_by(term,specimenID)%>%
summarize(meanViper=mean(metaviperscore))
lv2drug<-drugMeans%>%
ungroup()%>%
left_join(mp_res,by='specimenID')
corLvs<-lv2drug%>%
group_by(latent_var,term)%>%
summarize(lvDrugCor=cor(value,meanViper,method='spearman'))%>%
subset(abs(lvDrugCor)>0.65)%>%left_join(drugTargs,by='term')
DT::datatable(corLvs)There are sooo mnay drugs/LVs that are correlated. This is likely due to the high amount of overlap with drugs and targets.
Filtering for significant drug/lv combos
Since Robert calculated the enrichment of each LV by drug targets in syn21442304 we can select those with significant (p.adjust<0.01) enrichment that also have drugs that are somewhat recognizable (not CHEMBL ids or some form of Acid)
sigs<-synTableQuery("SELECT * from syn21442304 where \"p.adjust\"<0.01 and ID not like 'CHEMBL%' and ID not like '%Acid'")$asDataFrame()%>%
select(-c(ROW_ID,ROW_VERSION))##
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drugs<-subset(sigs,latent_var==lv)%>%
select(ID)%>%
distinct()
terms=subset(corLvs,latent_var==lv)%>%ungroup()%>%select(term)%>%distinct()
toplot=subset(lv2drug,latent_var==lv)%>%
subset(term%in%drugs$ID)%>%
subset(term%in%terms$term)%>%
rename(`LV Score`='value')
toplot$term<-droplevels(toplot$term)
p<-ggplot(toplot)+geom_point(aes(x=meanViper,y=`LV Score`,col=term,shape=tumorType))+
ggtitle(paste(lv,'correlated drugs by Viper score'))
print(p)
ggsave(paste(lv,'corDrugs.pdf',sep=''),useDingbats=FALSE)
p
})
sessionInfo()## R version 3.5.0 (2018-04-23)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS 10.14.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggplot2_3.2.1 feather_0.3.5 synapser_0.6.61 readr_1.3.1
## [5] tidyr_1.0.0 dplyr_0.8.3
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.3 later_1.0.0 pillar_1.4.2
## [4] compiler_3.5.0 PythonEmbedInR_0.3.37 tools_3.5.0
## [7] zeallot_0.1.0 digest_0.6.23 jsonlite_1.6
## [10] evaluate_0.14 lifecycle_0.1.0 tibble_2.1.3
## [13] gtable_0.3.0 pkgconfig_2.0.3 rlang_0.4.2
## [16] shiny_1.4.0 crosstalk_1.0.0 yaml_2.2.0
## [19] xfun_0.11 fastmap_1.0.1 withr_2.1.2
## [22] stringr_1.4.0 knitr_1.26 htmlwidgets_1.5.1
## [25] vctrs_0.2.0 hms_0.5.2 DT_0.10
## [28] grid_3.5.0 tidyselect_0.2.5 glue_1.3.1
## [31] R6_2.4.1 rmarkdown_1.17 farver_2.0.1
## [34] purrr_0.3.3 magrittr_1.5 promises_1.1.0
## [37] backports_1.1.5 scales_1.1.0 codetools_0.2-16
## [40] htmltools_0.4.0 assertthat_0.2.1 xtable_1.8-4
## [43] mime_0.7 colorspace_1.4-1 httpuv_1.5.2
## [46] labeling_0.3 stringi_1.4.3 pack_0.1-1
## [49] lazyeval_0.2.2 munsell_0.5.0 crayon_1.3.4